computing computational quantization deployment pruning

The gap

Supporting evidence — 2 representative gaps

• Generation of Fake Human Faces Using GAN’S (2026) · doi

  Model optimization techniques such as pruning, quantization, and knowledge distillation for lightweight GAN-based synthetic face generation have not been implemented or benchmarked. Specific deployment targets (mobile devices, edge devices) with computational constraints and latency requirements need to be tested.

  Keywords: model pruning quantization knowledge distillation lightweight deployment edge computing
• Multi-Modal Sentiment Analysis Using Text, Audio, And Facial Expressions for Human Emotion Detection - A Survey (2026) · doi

  Real-time and resource-constrained deployment optimization for the multimodal emotion recognition framework has not been addressed. Future research must conduct computational complexity analysis, model compression techniques (quantization, pruning, knowledge distillation), and latency benchmarking on edge devices and mobile platforms to enable practical deployment of the LSTM-based system in production environments.

  Keywords: real-time deployment resource constraints model compression edge computing latency optimization